Совершенствование управления краткосрочными активами предприятия (на примере ОАО «СтанкоГомель»)

Determining which reference genes have the highest stability, and are therefore appropriate for normalising data, is a crucial step in the design of real-time quantitative PCR (qPCR) gene expression studies. This is particularly warranted in non-model and ecologically important species for which appropriate reference genes are lacking, such as the mallard-a key reservoir of many diseases with relevance for human and livestock health. Previous studies assessing gene expression changes as a consequence of infection in mallards have nearly universally used β-actin and/or GAPDH as reference genes without confirming their suitability as normalisers. The use of reference genes at random, without regard for stability of expression across treatment groups, can result in erroneous interpretation of data. Here, eleven putative reference genes for use in gene expression studies of the mallard were evaluated, across six different tissues, using a low pathogenic avian influenza A virus infection model. Tissue type influenced the selection of reference genes, whereby different genes were stable in blood, spleen, lung, gastrointestinal tract and colon. β-actin and GAPDH generally displayed low stability and are therefore inappropriate reference genes in many cases. The use of different algorithms (GeNorm and NormFinder) affected stability rankings, but for both algorithms it was possible to find a combination of two stable reference genes with which to normalise qPCR data in mallards. These results highlight the importance of validating the choice of normalising reference genes before conducting gene expression studies in ducks. The fact that nearly all previous studies of the influence of pathogen infection on mallard gene expression have used a single, non-validated reference gene is problematic. The toolkit of putative reference genes provided here offers a solid foundation for future studies of gene expression in mallards and other waterfowl

Spring and Late Summer Phytoplankton Biomass Impact on the Coastal Sediment Microbial Community Structure

Two annual Baltic Sea phytoplankton blooms occur in spring and summer. The bloom intensity is determined by nutrient concentrations in the water, while the period depends on weather conditions. During the course of the bloom, dead cells sink to the sediment where their degradation consumes oxygen to create hypoxic zones (< 2 mg/L dissolved oxygen). These zones prevent the establishment of benthic communities and may result in fish mortality. The aim of the study was to determine how the spring and autumn sediment chemistry and microbial community composition changed due to degradation of diatom or cyanobacterial biomass, respectively. Results from incubation of sediment cores showed some typical anaerobic microbial processes after biomass addition such as a decrease in NO2− + NO3− in the sediment surface (0–1 cm) and iron in the underlying layer (1–2 cm). In addition, an increase in NO2− + NO3− was observed in the overlying benthic water in all amended and control incubations. The combination of NO2− + NO3− diffusion plus nitrification could not account for this increase. Based on 16S rRNA gene sequences, the addition of cyanobacterial biomass during autumn caused a large increase in ferrous iron-oxidizing archaea while diatom biomass amendment during spring caused minor changes in the microbial community. Considering that OTUs sharing lineages with acidophilic microorganisms had a high relative abundance during autumn, it was suggested that specific niches developed in sediment microenvironments. These findings highlight the importance of nitrogen cycling and early microbial community changes in the sediment due to sinking phytoplankton before potential hypoxia occurs

A Panel of Stably Expressed Reference Genes for Real-Time qPCR Gene Expression Studies of Mallards (Anas platyrhynchos)

Determining which reference genes have the highest stability, and are therefore appropriate for normalising data, is a crucial step in the design of real-time quantitative PCR (qPCR) gene expression studies. This is particularly warranted in non-model and ecologically important species for which appropriate reference genes are lacking, such as the mallard-a key reservoir of many diseases with relevance for human and livestock health. Previous studies assessing gene expression changes as a consequence of infection in mallards have nearly universally used β-actin and/or GAPDH as reference genes without confirming their suitability as normalisers. The use of reference genes at random, without regard for stability of expression across treatment groups, can result in erroneous interpretation of data. Here, eleven putative reference genes for use in gene expression studies of the mallard were evaluated, across six different tissues, using a low pathogenic avian influenza A virus infection model. Tissue type influenced the selection of reference genes, whereby different genes were stable in blood, spleen, lung, gastrointestinal tract and colon. β-actin and GAPDH generally displayed low stability and are therefore inappropriate reference genes in many cases. The use of different algorithms (GeNorm and NormFinder) affected stability rankings, but for both algorithms it was possible to find a combination of two stable reference genes with which to normalise qPCR data in mallards. These results highlight the importance of validating the choice of normalising reference genes before conducting gene expression studies in ducks. The fact that nearly all previous studies of the influence of pathogen infection on mallard gene expression have used a single, non-validated reference gene is problematic. The toolkit of putative reference genes provided here offers a solid foundation for future studies of gene expression in mallards and other waterfowl

RNA transcript sequencing reveals inorganic sulfur compound oxidation pathways in the acidophile Acidithiobacillus ferrivorans

Acidithiobacillus ferrivorans is an acidophile implicated in low-temperature biomining for the recovery of metals from sulfide minerals. Acidithiobacillus ferrivorans obtains its energy from the oxidation of inorganic sulfur compounds, and genes encoding several alternative pathways have been identified. Next-generation sequencing of At. ferrivorans RNA transcripts identified the genes coding for metabolic and electron transport proteins for energy conservation from tetrathionate as electron donor. RNA transcripts suggested that tetrathionate was hydrolyzed by the tetH1 gene product to form thiosulfate, elemental sulfur and sulfate. Despite two of the genes being truncated, RNA transcripts for the SoxXYZAB complex had higher levels than for thiosulfate quinone oxidoreductase (doxDA genes). However, a lack of heme-binding sites in soxX suggested that DoxDA was responsible for thiosulfate metabolism. Higher RNA transcript counts also suggested that elemental sulfur was metabolized by heterodisulfide reductase (hdr genes) rather than sulfur oxygenase reductase (sor). The sulfite produced as a product of heterodisulfide reductase was suggested to be oxidized by a pathway involving the sat gene product or abiotically react with elemental sulfur to form thiosulfate. Finally, several electron transport complexes were involved in energy conservation. This study has elucidated the previously unknown At. ferrivorans tetrathionate metabolic pathway that is important in biomining

GeNorm stability rankings (M value) of eleven candidate reference genes amongst six mallard tissues.

<p>(A) Blood. (B) Spleen. (C) Liver. (D) GI2 (distal jejunum). (E) GI4 (distal ileum). (F) Colon. Data is plotted from least stable (left) to most stable (right) gene. Genes with an M value below 0.5 (red dashed line) are considered stable.</p

Gene expression papers in Mallard and Pekin ducks.

<p>This summary is limited to those studies assessing mRNA transcriptional changes of genes of interest in response to infection of live animals with a pathogen followed by qPCR profiling of gene expression.</p

NormFinder stability rankings of eleven candidate reference genes amongst six mallard tissues.

<p>(A) Blood. (B) Spleen. (C) Liver. (D) GI2 (distal jejunum). (E) GI4 (distal ileum). (F) Colon. Data is plotted from least stable (left) to most stable (right) gene.</p

The number of RGs required for data normalisation.

<p>Y-axis represents the GeNorm V value and the X-axis is V_i/j where “<i>i</i>” is V calculated for <i>n</i> genes and “<i>j</i>” is <i>n</i> + 1 genes. If the V value for a given comparison of V_i/j falls below 0.15 (red dashed line), then the “<i>i</i>” number of genes is sufficient for normalisation.</p

Selected reference genes per tissue.

<p>Combined stability value for the best combination of genes as calculated in (A) GeNorm and (B) NormFinder. For GeNorm, the number selected was that required to reach a threshold stability V value of lower than 0.15; for NormFinder the recommended combination of the two best genes are provided. Note that stability values are not directly comparable between GeNorm and NormFinder, as each algorithm uses its own stability index.</p